# Bootstrapping a Crosslingual Semantic Parser

arXiv: https://arxiv.org/abs/2004.02585 · HTML: https://mlsanity.com/paper/2004.02585/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages](https://mlsanity.com/paper/2203.08388/) (2022)
- [XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2210.13693/) (2022)
- [MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2212.13492/) (2022)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents](https://mlsanity.com/paper/2306.17674/) (2023)
- [Meta-Learning a Cross-lingual Manifold for Semantic Parsing](https://mlsanity.com/paper/2209.12577/) (2022)
- [Multilingual Neural Semantic Parsing for Low-Resourced Languages](https://mlsanity.com/paper/2106.03469/) (2021)
- [XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations](https://mlsanity.com/paper/2306.04085/) (2023)
- [Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation](https://mlsanity.com/paper/2302.09424/) (2023)
- [The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning](https://mlsanity.com/paper/2305.12737/) (2023)
- [Bootstrapping Multilingual Semantic Parsers using Large Language Models](https://mlsanity.com/paper/2210.07313/) (2022)
- [Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2307.04096/) (2023)
- [Cross-Lingual QA as a Stepping Stone for Monolingual Open QA in Icelandic](https://mlsanity.com/paper/2207.01918/) (2022)

## Abstract (arXiv)

Recent progress in semantic parsing scarcely considers languages other than English but professional translation can be prohibitively expensive. We adapt a semantic parser trained on a single language, such as English, to new languages and multiple domains with minimal annotation. We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models. We develop a Transformer-based parser combining paraphrases by ensembling attention over multiple encoders and present new versions of ATIS and Overnight in German and Chinese for evaluation. Experimental results indicate that MT can approximate training data in a new language for accurate parsing when augmented with paraphrasing through multiple MT engines. Considering when MT is inadequate, we also find that using our approach achieves parsing accuracy within 2% of complete translation using only 50% of training data.